lly prominent when conservative treatment was employed for mild cases.Pregnancy is associated with changes in bone remodeling and calcium metabolism, which may increase the risk of fragility fracture after menopause. We hypothesized that in postmenopausal women, with history of grand multiparity, the magnitude of trabecular bone deterioration is associated with number of deliveries.
1217 women aged 69.2±6.4years, from the Bushehr Elderly Health (BEH) program were recruited. The areal bone mineral density (aBMD) of the lumbar spine and femoral neck and trabecular bone score (TBS) of 916 postmenopausal women, with grand multiparity defined as more than 4 deliveries, were compared with those of 301 postmenopausal women with 4 or fewer deliveries. The association of multiparity with aBMDs and TBS were evaluated after adjustment for possible confounders including age, years since menopause, body mass index, and other relevant parameters.
The aBMD of femoral neck (0.583±0.110 vs. 0.603±0.113g/cm), lumbar spine (0.805±0.144 vs. 0.829±0.140g/cm) and TBS (1.234±0.086 vs. 1.260±0.089) were significantly lower in women with history of grand multiparity than others. In the multiple regression analysis, after adjusting for confounders, the negative association did persist for lumbar spine aBMD (beta=-0.02, value=0.01), and the TBS (beta=-0.01, p value=0.03), not for femoral neck aBMD.
We infer that grand multiparity have deleterious effects on the aBMD and the trabecular pattern of the lumbar spine.
We infer that grand multiparity have deleterious effects on the aBMD and the trabecular pattern of the lumbar spine.Dual-energy X-ray absorptiometry (DXA) is the gold standard imaging method for diagnosing osteoporosis in clinical practice. The DXA images are commonly used to estimate a numerical value for bone mineral density (BMD), which decreases in osteoporosis. Low BMD is a known risk factor for osteoporotic fractures. In this study, we used deep learning to identify lumbar scoliosis and structural abnormalities that potentially affect BMD but are often neglected in lumbar spine DXA analysis. In addition, we tested the approach's ability to predict fractures using only DXA images. A dataset of 2949 images gathered by Kuopio Osteoporosis Risk Factor and Prevention Study was used to train a convolutional neural network (CNN) for classification. The model was able to classify scoliosis with an AUC of 0.96 and structural abnormalities causing unreliable BMD measurement with an AUC of 0.91. It predicted fractures occurring within 5 years from the lumbar spine DXA scan with an AUC of 0.63, meeting the predictive performance of combined BMD measurements from the lumbar spine and hip. In an independent test set of 574 clinical patients, AUC for lumbar scoliosis was 0.93 and AUC for unreliable BMD measurements was 0.94. In each classification task, neural network visualizations indicated the model's predictive strategy. We conclude that deep learning could complement the well established DXA method for osteoporosis diagnostics by analyzing incidental findings and image reliability, and improve its predictive ability in the future.This paper focuses on the study of environmental risk assessment and comprehensive index model of disaster loss for COVID-19 transmission. Considering the five environmental vectors of carrier vulnerability, environmental instability of pregnancy and disaster, intensity of disaster-causing factors, disaster prevention and mitigation capacity and emergency prevention and control capacity and its 38 indicators, the correlation coefficient matrix and principal component expressions of each vector are established by principal component analysis, respectively, and the index model of each vector is established on the basis. Then, considering the index models of these five vectors, we established the disaster loss composite index model, which was used to conduct environmental risk assessment and disaster loss composite index analysis of the transmission of COVID-19 in Hubei Province during the period of January 21, 2020 to March 18, 2020. The empirical study showed that (1) the risk index peaked from January 21 to January 23; (2) the risk index was at a low but volatile level from January 24 to March 14; (3) the risk index rose again slightly from March 15 and rose to another peak on March 16. These fluctuating, smooth and fluctuating processes of the comprehensive index of disaster losses of COVID-19 in Hubei Province are basically stable and consistent with the actual situation of the virus outbreak in the early stage, isolation and prevention and control in the middle stage, and resumption of work and production in the late stage. The study in this paper provides a scientific decision-making reference for the prevention and control of COVID-19 as well as emergency prevention and control measures.To investigate whether the attenuation value obtained by subtracting the CT value obtained from abdominal dynamic contrast enhanced (ADCE)-MDCT imaging of the equilibrium phase from the value obtained from that of the portal phase in hepatic parenchyma is useful in distinguishing normal liver from liver cirrhosis (LC) and to predict the development of esophageal varices (EVs) in patients with LC.
We assigned 72 outpatients to group A (n = 45; normal liver) and group B (n = 27; LC), who underwent ADCE-MDCT. The attenuation value and CT value of the hepatic parenchymal portal and equilibrium phase were compared, and the correlation between attenuation value and biomarkers (ALB, T-bil, PLT, FIB-4, APRI, and AAR) was investigated. Furthermore, we investigated differences in the attenuation value, FIB-4, APRI, and AAR between the two subgroups of group B [without EVs (group a) and with EVs (group b)]. We performed receiver operating characteristic (ROC) analysis of the attenuation value, FIB-4, APRI, and, AAR for subgroup a vs b and evaluated the diagnostic accuracy.
Significant differences were observed between groups A and B in all items. https://www.selleckchem.com/products/cremophor-el.html The attenuation value correlated with ALB, T-bil, PLT, FIB-4, and APRI. Only attenuation value showed a significant difference between groups a and b. The best cut-off attenuation value, FIB-4, APRI, and AAR for predicting EVs, according to ROC analysis was 13.4 HU, 6.8, 1.9, and 1.5.
Attenuation value can be useful to quantitatively classify normal liver and LC and to predict EVs in patients with LC.
Attenuation value can be useful to quantitatively classify normal liver and LC and to predict EVs in patients with LC.